Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Computing
J. Latha, S. Karthick Murugan, Sheela Jayachandran, G. Uma Maheshwari, G. M. Naveen, S. Navaneethan · 2024
In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system’s performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing.